Tracking-as-Recognition for Articulated Full-Body Human Motion Analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model. Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model. The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Patrick Peursum, Svetha Venkatesh, Geoff A. W. West
• Keywords: Accurate Tracking, Annealed Particle Filter, Clean Observing Conditions, Computer Vision, CVPR 2007, Hidden Markov Model, Long-term Tracking